calc.engineer

heatsink cfd

heatsink_cfd

Analyze a forced-convection fin-array heatsink using established channel-flow correlations (developing laminar Nusselt, fin efficiency tanh(mH)/mH, parallel-plate friction). Computes base-to-air thermal resistance, pressure drop, base and fin-tip temperatures, fin efficiency, and the optimal fin spacing for the given airflow. Use for heatsink selection and first-pass optimization in electronics cooling. Runs instantly in-worker; mesh-level CFD via container backend is planned.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`fin_count`	Yes	Number of fins
`heat_load_w`	Yes	Total heat load applied to base in watts
`base_width_m`	Yes	Heatsink base width in metres
`fin_height_m`	Yes	Fin height in metres
`mesh_density`	No	Mesh density	medium
`base_height_m`	Yes	Base plate thickness in metres
`base_length_m`	Yes	Heatsink base length in metres
`ambient_temp_c`	No	Ambient air temperature in Celsius
`fin_thickness_m`	Yes	Fin thickness in metres
`airflow_velocity_ms`	Yes	Incoming airflow velocity in m/s

Output Schema

TableJSON Schema

Name	Required	Description
`warnings`	Yes	Solver warnings
`mesh_cells`	Yes	Mesh cell count
`runtime_ms`	Yes	Simulation time in ms
`fin_efficiency`	Yes	Fin efficiency (0-1)
`fin_tip_temp_c`	Yes	Average fin tip temperature (°C)
`avg_base_temp_c`	Yes	Average base temperature (°C)
`max_base_temp_c`	Yes	Maximum base temperature (°C)
`pressure_drop_pa`	Yes	Pressure drop across the heatsink (Pa)
`optimal_fin_spacing_m`	Yes	Suggested optimal fin spacing for this airflow (m)
`thermal_resistance_cw`	Yes	Thermal resistance from base to air (°C/W)

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains the correlation-based method and states it runs instantly, which is transparent. Without annotations, it carries the burden; it could be improved by noting assumptions (e.g., developing laminar flow) but is still adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (four sentences) with no unnecessary words. It front-loads the purpose and includes key details like method, outputs, and run-time behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (10 parameters, no annotations, but has output schema), the description covers the purpose, method, outputs, and use case comprehensively. It is complete for a calculation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Parameter descriptions are fully covered in the input schema, so the description adds minimal per-parameter detail. The formulas mentioned (e.g., fin efficiency) provide background but not additional semantics for each parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes a forced-convection fin-array heatsink using correlations, with specific outputs like thermal resistance and optimal spacing. It is distinctly different from sibling tools such as pcb_thermal or thermal_enclosure.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It specifies use for 'heatsink selection and first-pass optimization in electronics cooling', providing clear context. However, it does not explicitly mention when not to use or compare to alternatives like detailed CFD.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation4/5

Despite 89 tools, each has a clearly distinct purpose with detailed descriptions that often reference related tools. Overlap exists (e.g., multiple LoRa/RF tools), but the descriptions are sufficient to distinguish them. Some confusion possible among similar-sounding tools like attenuator_pi and attenuator_tee, but the descriptions explicitly compare them.

Naming Consistency4/5

Consistent underscore-separated lowercase naming. Most tools follow a verb_noun pattern (e.g., capacitor_charge, wire_gauge) or noun_noun (power_cost). Minor inconsistencies such as 'bmi_calculator' vs 'solar_sizing' but overall predictable.

Tool Count2/5

89 tools is far too many for a single MCP server. This scope is more appropriate for multiple specialized servers. The sheer number will slow agent selection and increase cognitive load, reducing coherence.

Completeness3/5

Covers many domains (RF, solar, PCB, networking, math, etc.) but lacks depth in some areas (e.g., no three-phase power, no airflow calculations). Some domains have comprehensive coverage (LoRa/Meshtastic), but others feel incomplete for the tool count.

Resources

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